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distributed Domain Generation for Large-Scale Scientific computing 20

Distributed Domain Generation for Large-Scale Scientific Com...

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20th international symposium on parallel and distributed computing (ISPDC)

作者： Ertl, Christoph Mundani, Ralf-Peter Tech Univ Munich Chair Computat Modeling & Simulat Munich Germany Univ Appl Sci Grisons Swiss Inst Informat Sci Chur Switzerland

ISBN: (纸本)9781665432818

In this work, we present methods for distributed domain generation within the constraints of our decentral domain management concept. Here, all participating actors only have knowledge of their immediate neighbours, which are defined by geometric and hierarchical relations between nodes that represent subsets of the computational domain. We generate this domain following a hierarchical spacetree refinement. First, an initial tree is generated on every participating process. Second, this tree is distributed following a space-filling curve linearisation locally. Every process is assigned at least one leaf node of the initial tree, which acts as a starting point for the subsequent domain generation. From here, every process independently refines a subdomain using a decomposition method, which transforms a triangular surface-based geometry description into a volume-based one, using increasingly complex intersection tests. The resulting domain tree is distributed, yet neighbourhood references of neighbouring subtrees are not resolved. We combine the resolution of these relations with a 2:1 tree balancing, which involves the transfer of the surface of neighbouring subtrees. We provide results of a domain generation testcase, using an input geometry with 84,072 triangles on up to 896 processes of the CoolMUC-2 cluster segment of LRZ's Linux Cluster System. Here, we bring down the overall time it takes to generate an adaptively refined and balanced octree with depth d = 7 from 5.5 hours on one process to two seconds on 896 processes.

关键词： Large-scale scientific computing parallel algorithms distributed algorithms distributed systems distributed domain generation spacetrees

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Intelligent judgment of rotating machinery based on multi-scale parallel network and attention mechanism 21

Intelligent judgment of rotating machinery based on multi-sc...

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21st international symposium on distributed computing and Applications for Business Engineering and Science, DCABES 2022

作者： Fan, Zhixiang Qian, Pengjiang Jiangnan University School of Artificial Intelligence and Computer Science Wuxi China

ISBN: (纸本)9781665454629

The primary problem solved in rotating machinery fault diagnosis is how to effectively extract fault features from the vibration signals with noise. To extract fault features accurately, this study proposes a multi-scale parallel convolutional neural network fault recognition algorithm, which can carry out feature fusion. The above method combines empirical feature extraction (e.g., fast Fourier transform) to enrich feature information, which can effectively implement deep learning. The effectiveness and reliability of the method are verified through example studies on JNU, SEU and PU rolling bearing experimental data sets. The algorithm has the higher classification capability and diagnostic accuracy compared with four common deep learning algorithms. © 2022 IEEE.

关键词： Deep neural networks

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Data-Intensive computing Modules for Teaching parallel and distributed computing

Data-Intensive Computing Modules for Teaching Parallel and D...

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35th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Gowanlock, Michael Gallet, Benoit No Arizona Univ Sch Informat Comp & Cyber Syst Flagstaff AZ 86011 USA

ISBN: (纸本)9781665435772

parallel and distributed computing (PDC) has found a broad audience that exceeds the traditional fields of computer science. This is largely due to the increasing computational demands of many engineering and domain science research objectives. Thus, there is a demonstrated need to train students with and without computer science backgrounds in core PDC concepts. Given the rise of data science and other data-enabled computational fields, we propose several data-intensive pedagogic modules that are used to teach PDC using message-passing programming with the Message Passing Interface (MPI). These modules employ activities that are common in database systems and scientific workflows that are likely to be employed by domain scientists. Our hypothesis is that using application-driven pedagogic materials facilitates student learning by providing the context needed to fully appreciate the goals of the activities. We evaluated the efficacy of using the data-intensive pedagogic modules to teach core PDC concepts using a sample of graduate students enrolled in a high performance computing course at Northern Arizona University. In the sample, only 30% of students have a traditional computer science background. We found that the hands-on application-driven approach was generally successful at helping students learn core PDC concepts.

关键词： Computer Science Education Data-Intensive computing High Performance computing parallel and distributed computing

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Performance Analysis and Optimization of Nvidia H100 Confidential computing for AI Workloads

Performance Analysis and Optimization of Nvidia H100 Confide...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Yifan Tan Zeyu Mi Institute of Parallel and Distributed Systems SEIEE Shanghai Jiao Tong University

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

NVIDIA’s H100 Confidential computing (CC) counters the security hazards inherent in cloud AI workloads. It enforces data encryption to achieve data confidentiality, which leads to substantial throughput reductions as high as 93% in various AI workloads (such as TensorRT, PEFT and vLLM). Confronting this substantial overhead issue, we first delve into the underlying causes through meticulous analysis. This groundwork enables us to devise an innovative runtime system that operates seamlessly in the background, completely transparent to end-users. The cornerstone of our system lies in leveraging multiple encryption workers. Experiments demonstrate that our solution effectively reduces throughput drop to less than 28.1%.

关键词： Performance evaluation distributed processing Runtime Graphics processing units Throughput Hazards Encryption Performance analysis Artificial intelligence Optimization

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PROV-IO: An I/O-Centric Provenance Framework for Scientific Data on HPC Systems 22

PROV-IO: An I/O-Centric Provenance Framework for Scientific ...

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31st international symposium on High-Performance parallel and distributed computing (HPDC)

作者： Han, Runzhou Byna, Suren Tang, Houjun Dong, Bin Zheng, Mai Iowa State Univ Ames IA 50011 USA Lawrence Berkeley Natl Lab Berkeley CA USA

ISBN: (纸本)9781450391993

Data provenance, or data lineage, describes the life cycle of data. In scientific workflows on HPC systems, scientists often seek diverse provenance (e.g., origins of data products, usage patterns of datasets). Unfortunately, existing provenance solutions cannot address the challenges due to their incompatible provenance models and/or system implementations. In this paper, we analyze three representative scientific workflows in collaboration with the domain scientists to identify concrete provenance needs. Based on the first-hand analysis, we propose a provenance framework called PROV-IO, which includes an I/O-centric provenance model for describing scientific data and the associated I/O operations and environments precisely. Moreover, we build a prototype of PROV-IO to enable end-to-end provenance support on real HPC systems with little manual effort. The PROV-IO framework provides flexibility in selecting various classes of provenance. Our experiments with realistic workflows show that PROV-IO can address the provenance needs of the domain scientists effectively with reasonable performance (e.g., less than 3.5% tracking overhead for most experiments). Moreover, PROV-IO outperforms a state-of-the-art system (i.e., ProvLake) in our experiments.

关键词： Data Provenance Lineage Scientific Data Workflows High Performance computing FAIR Principles Explainability Trustworthiness

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Ensemble-Based System Benchmarking for HPC

Ensemble-Based System Benchmarking for HPC

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international symposium on parallel and distributed computing

作者： Anna Fuchs Jannek Squar Michael Kuhn Universität Hamburg Hamburg Germany Otto von Guericke University of Magdeburg Magdeburg Germany

ISBN: (数字)9798350369199

ISBN: (纸本)9798350369205

In HPC supercomputers, the CPU, memory, network and storage play a critical role in application performance. Established benchmarks measure the theoretical peak performance of these components, but storage benchmarks often focus solely on I/O and lack realism. To address this, we present numio, a benchmark that simulates and evaluates overlapping compute, communication and I/O phases. Furthermore, we present a novel ensemble-driven system benchmarking strategy. This approach involves running multiple benchmarks in parallel to analyse their interactions and assess the system’s ability to handle the workload. Using a real-world example, we demonstrate how this approach reveals performance issues in complex HPC systems that remain hidden when using traditional methods using isolated benchmarks on empty systems.

关键词： Economics Data centers Runtime File systems System performance Estimation Benchmark testing Supercomputers Software distributed computing

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An asynchronous distributed-memory optimization solver for two-stage stochastic programming problems 20

An asynchronous distributed-memory optimization solver for t...

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20th international symposium on parallel and distributed computing (ISPDC)

作者： Wang, Jingyi Chiang, Nai-Yuan Petra, Cosmin G. Lawrence Livermore Natl Lab Ctr Appl Sci Comp Livermore CA 94550 USA

ISBN: (纸本)9781665432818

We present a scalable optimization algorithm and its parallel implementation for two-stage stochastic programming problems of large-scale, particularly the security constrained optimal power flow models routinely used in electrical power grid operations. Such problems can be prohibitively expensive to solve on industrial scale with the traditional methods or in serial. The algorithm decomposes the problem into first-stage and second-stage optimization subproblems which are then scheduled asynchronously for efficient evaluation in parallel. Asynchronous evaluations are crucial in achieving good balancing and parallel efficiency because the second-stage optimization subproblems have highly varying execution times. The algorithm employs simple local second-order approximations of the second-stage optimal value functions together with exact first- and second-order derivatives for the first-stage subproblems to accelerate convergence. To reduce the number of the evaluations of computationally expensive second-stage subproblems required by line search, we devised a flexible mechanism for controlling the step size that can be tuned to improve performance for individual class of problems. The algorithm is implemented in C++ using MPI non-blocking calls to overlap computations with communication and boost parallel efficiency. Numerical experiments of the algorithm are conducted on Summit and Lassen supercomputers at Oak Ridge and Lawrence Livermore National Laboratories and scaling results show good parallel efficiency.

关键词： SCACOPF Optimization parallelization

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RAW 2024 Invited Talk-3: Self-aware Reliable and Reconfigurable computing Systems — An Overview

RAW 2024 Invited Talk-3: Self-aware Reliable and Reconfigura...

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Diana Göhringer TU Dresden

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

In demanding environments like space, avionics or robotics, computing systems face challenges affecting their lifetime and reliability. Traditional approaches like static hardware redundancy prove ineffective for modern, complex systems. This talk presents an overview of self-aware, reliable, and reconfigurable computing systems leveraging Field Programmable Gate Arrays (FPGAs). Despite increasing application demands, FPGA resources remain limited, posing challenges in implementing static redundancy alongside high performance and energy efficiency. Addressing these challenges requires innovative resilience concepts, emphasizing adaptability, reliability, and energy optimization. This talk surveys existing literature and identifies open challenges in achieving resilient computing systems for demanding applications.

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Automatic aggregation of subtask accesses for nested OpenMP-style tasks 34

Automatic aggregation of subtask accesses for nested OpenMP-...

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34th IEEE international symposium on Computer Architecture and High Performance computing (SBAC-PAD)

作者： Shaaban, Omar Aguilar, Jimmy Beltran, Vicenc Carpenter, Paul Ayguade, Eduard Labarta Mancho, Jesus Barcelona Supercomp Ctr Barcelona Spain

ISBN: (数字)9781665451550

ISBN: (纸本)9781665451550

Task-based programming is a high performance and productive model to express parallelism. Tasks encapsulate work to be executed across multiple cores or offloaded to GPUs, FPGAs, other accelerators or other nodes. In order to maintain parallelism and afford maximum freedom to the scheduler, the task dependency graph should be created in parallel and well in advance of task execution. A key limitation with OpenMP and OmpSs-2 tasking is that a task cannot be created until all its accesses and its descendents' accesses are known. Current approaches to work around this limitation either stop task creation and execution using a taskwait or they substitute "fake" accesses known as sentinels. This paper proposes the auto clause, which indicates that the task may create subtasks that access unspecified memory regions or it may allocate and return memory at addresses that are of course not yet known. Unlike approaches using taskwaits, there is no interruption to the concurrent creation and execution of tasks, maintaining parallelism and the scheduler's ability to optimize load balance and data locality. Unlike existing approaches using sentinels, all tasks can be given a precise specification of their own data accesses, so that a single mechanism is used to control task ordering, program data transfers on distributed memory and optimize data locality, e.g. on NUMA systems. The auto clause also provides an incremental path to develop programs with nested tasks, by removing the need for every parent task to have a complete specification of the accesses of its descendent tasks. This is redundant information that can be time consuming and error-prone to describe. We present a straightforward runtime implementation that achieves a 1.4 times speedup for n-body with OmpSs-2@Cluster task offloading to 32 nodes and <4% slowdown for three benchmarks with task offloading to 8 nodes. All code is open source.

关键词： HPC Runtime Systems OpenMP OmpSs-2 Task parallelism distributed computing

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Visualizing PRAM Algorithm for Mergesort

Visualizing PRAM Algorithm for Mergesort

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Cade Wiley Grey Ballard Department of Computer Science Wake Forest University Winston-Salem NC USA

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

Undergraduate algorithms courses are a natural setting for teaching many of the theoretical ideas of parallel computing. Mergesort is a fundamental sequential divide-and-conquer algorithm often analyzed in such courses. In this work, we present a visualization tool to help demonstrate a novel PRAM algorithm for mergesort that is work efficient and has polylogarithmic span. Our implementation uses the Thread -Safe Graphics Library, which has an existing visualization of parallel mergesort. We demonstrate that our proposed algorithm has better work and span than the one currently visualized.

关键词： Visualization distributed processing Instruction sets Merging Education Forestry parallel processing

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